Abstract for treece_tr624

Ultrasound strain imaging is becoming increasingly popular as a way to
measure stiffness variation in soft tissue. Almost all techniques
involve the estimation of a field of relative displacements between
measurements of tissue undergoing different deformations. These
estimates are often high resolution, but some form of smoothing is
required to increase the precision, either by direct filtering or as
part of the gradient estimation process. Such methods generate uniform
resolution images, but strain quality typically varies considerably
within each image, hence a trade-off is necessary between increasing
precision in the low quality regions and reducing resolution in the high
quality regions. We introduce a smoothing technique, developed from the
nonparametric regression literature, which can avoid this trade-off by
generating uniform precision images. In such an image, high resolution
is retained in areas of high strain quality but sacrificed for the sake
of increased precision in low quality areas. We contrast the algorithm
with other methods on simulated, phantom and clinical data, for both 2D
and 3D strain imaging. We also show how the technique can be e±ciently
implemented at real time rates with realistic parameters on modest
hardware. Uniform precision nonparametric regression promises to be a
useful tool in ultrasound strain imaging.

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